Udemy

Real-World Object Detection: Waste Sorting & Tomato Ripeness

Use YOLO and computer vision to build object detection systems for smart waste management and tomato ripeness detection.
New
Rating: 5.0 out of 5 (1 rating)
519 students
42min of on-demand video
English
English [Auto]

Build real-world object detection models using YOLOv10 for waste sorting and tomato ripeness detection.
Annotate custom image datasets using Annotate-Lab in YOLO format for object detection tasks.
Apply data augmentation techniques to improve model accuracy and robustness.
Deploy trained models to the web and mobile using Gradio, Hugging Face and Flutter for real-time AI applications.

Requirements

  • Basic Python knowledge is helpful, but the course is beginner-friendly and guides you step-by-step.
  • A computer with internet access and basic familiarity with running Python scripts.
  • Interest in AI, computer vision, or sustainability-related projects.

Description

Are you ready to apply computer vision to real-world problems?
In this hands-on course, you’ll build two complete object detection projects: one for identifying household waste items (like plastic, glass, and paper), and another for detecting ripe and unripe tomatoes using the latest YOLOv10 model.

We’ll walk you through each step of the pipeline from dataset preparation and annotation to training and deploying your own AI models. You'll gain practical experience with tools like Annotate Lab, Gradio, and Ultralytics YOLO, while also learning how data augmentation and evaluation metrics can improve model performance.

Whether you're interested in sustainability, agriculture, or real-time AI applications, this course provides both the theory and implementation you need to bring AI to life.

By the end of this course, you will:

  • Train a YOLOv10 model to detect ripe vs. unripe tomatoes

  • Build an object detector for sorting waste categories

  • Annotate images using Annotate-Lab with YOLO format

  • Apply data augmentation to boost performance

  • Deploy your model using Gradio on Hugging Face Spaces

  • Export and run your model on mobile devices (optional module)

This course is ideal for:

  • Developers and data scientists curious about object detection

  • Environmental and agri-tech enthusiasts

  • Anyone looking to learn YOLOv10 with practical projects

Enroll today and build AI tools that make an impact from waste bins to tomato fields.

Who this course is for:

  • Beginners and intermediate learners interested in applying computer vision to real-world problems.
  • Developers, students, and enthusiasts looking to build practical AI projects in sustainability and agriculture.
  • Educators and researchers interested in hands-on projects for waste detection and smart farming.
  • Anyone curious about YOLO, object detection, or using AI for environmental impact.

Instructor

Researcher | Author | Software Engineer
  • 4.5 Instructor Rating
  • 119 Reviews
  • 5,944 Students
  • 2 Courses

Suman has over 10 years of experience in the IT industry, specializing in AI and software engineering. He is the creator of D.Waste, an AI-powered platform for waste management. Suman has also developed a canvas game that integrates AI, which secured third place in the 2019 Developer Circles from Facebook Community Challenge in the Asia Pacific region.

Suman is the author of Learn JavaScript: Beginners Edition. Outside of his professional work, he is passionate about building developer tools to increase productivity and is dedicated to sustainable living and mountaineering.

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